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dc.contributor.authorZhang, Ziyien_US
dc.description.abstractIn this final year project, several testing scenarios and related methodology have been designed to examine the performance of the cutting-edge neural networks for monocular depth estimation. Since neural networks for monocular depth estimation is a fast-developing and emerging research field in recent years, neural network design and techniques involved keep evolving. It is both reasonable and beneficial to perceive different novel network design and implement these networks personally. If all the parameters during testing meet the lowest expectations in relative real-life application scenarios, it can be expected that neural networks will replace the dedicated depth sensors and make a huge difference in high-tech fields like artificial intelligence and autonomous driving.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleEvaluation and comparison of various deep neural networks for monocular depth estimationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Hanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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